Influence of weather on aspects of driving behaviour at an urban intersection

Marek Junghans; Claudia Leschik; Clemens Schicktanz; Peter Wagner

doi:10.55329/kbji6643

Authors

Marek Junghans German Aerospace Center (DLR), Germany https://orcid.org/0000-0003-2019-401X
Claudia Leschik German Aerospace Center (DLR), Germany https://orcid.org/0000-0001-8232-9608
Clemens Schicktanz German Aerospace Center (DLR), Germany https://orcid.org/0000-0002-3234-2086
Peter Wagner German Aerospace Center (DLR), Germany | Technical University of Berlin, Germany

DOI:

https://doi.org/10.55329/kbji6643

Keywords:

car-following (CF), driving behaviour, road user trajectories, surrogate measures of safety (SMoS), weather influence, Western Europe

Abstract

Weather conditions increase crash risk due to decreased visibility, changing vehicle dynamics, and driving behaviour. It remains unclear how different weather conditions affect non-crash driving behaviour, whose understanding is essentially important for the genesis of crashes, and for the development of automated driving functions (ADFs). In this study the influence of 25 different weather conditions on driving behaviour at an urban intersection in Germany was quantified using selected surrogate measures of safety (SMoS). For this purpose, eight months of combined trajectory and weather data were used. For each interaction the most critical moments defined by the minimum time-to-collision, and the associated kinematic profiles in that moment were evaluated. The findings suggest measurable and significant differences in driving behaviour across all weather types, indicating that drivers adjust speed, acceleration, and spatio-temporal distances in response to weather, but particularly in response to their manoeuvres. Apparently, these responses have no negative effect on road safety. However, the results extend the current state of the art by quantifying weather- and manoeuvre-related behavioural adaptation in non-crash situations and by identifying potential implications for road infrastructure.

Downloads

Download data is not yet available.

Author Biographies

Marek Junghans, German Aerospace Center (DLR), Germany

Marek Junghans is a research associate at German Aerospace Center (DLR), Institute of Transportation Systems. He received his Doctorate (Dr.-Ing.) from Dresden University of Technology in Intelligent Transportation Systems. His research interests cover stochastic signal processing and traffic safety with strong focus on cycling safety, measuring and understanding traffic behaviour to improve safety.

CRediT contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing.

Claudia Leschik, German Aerospace Center (DLR), Germany

Claudia Leschik works as a research associate at the Institute of Transportation Systems at German Aerospace Center (DLR). Her research focuses on trajectory analysis and modelling of general driving, cycling behaviour, and the interaction behaviour of vulnerable road users, primarily cyclists.

CRediT contribution: Conceptualization, Data curation, Methodology, Resources, Visualization, Writing – review & editing.

Clemens Schicktanz, German Aerospace Center (DLR), Germany

Clemens Schicktanz is Ph.D. candidate at Institute of Transportation Systems at German Aerospace Center (DLR). His research focuses on modelling road user behaviour based on trajectory data and scenario-based testing of automated vehicles.

CRediT contribution: Writing – review & editing.

Peter Wagner, German Aerospace Center (DLR), Germany | Technical University of Berlin, Germany

Peter Wagner is a physicist at German Aerospace Center (DLR), Institute of Transportation Systems, and an honouree professor at the Technical University of Berlin. His research focuses on traffic safety, traffic modelling in general, and traffic simulation.

CRediT contribution: Conceptualization, Formal analysis, Methodology, Supervision, Writing – review & editing.

References

Abdel Madjid, N., Ahmad, A., Mebrahtu, M., Babaa, Y., Nasser, A., Malik, S., Hassan, B., Werghi, N., Dias, J., & Khonji, M. (2026). Trajectory prediction for autonomous driving: Progress, limitations, and future directions. Information Fusion, 126, 103588.

Azar, F. (2025). Left-Turn Collisions: A Leading Cause Of Serious Injuries. Accessed Jan 9, 2026.

Becker, N., Rust, H. W., & Ulbrich, U. (2022). Weather impacts on various types of road crashes: a quantitative analysis using generalized additive models. Eur. Transp. Res. Rev., 14, V37. DOI: https://doi.org/10.1186/s12544-022-00561-2

Behbahani, H., Nadimi, N., Alnoori, H., & Sayadi, M. (2014). Developing a new surrogate safety indicator based on motion equations. Promet – Traffic – Traffico, 26, 371-381. DOI: https://doi.org/10.7307/ptt.v26i5.1388

Belin, M. Å., Tillgren, P., & Vedung, E. (2011). Vision Zero – a road safety policy innovation. International Journal of Injury Control and Safety Promotion, 19(2), 171-179. DOI: https://doi.org/10.1080/17457300.2011.635213

Bonela, S. R., & Kadali, B. R. (2022). Review of traffic safety evaluation at T-intersections using surrogate safety measures in developing countries context. IATSS Research, 46(3), 307-321. DOI: https://doi.org/10.1016/j.iatssr.2022.03.001

Broughton, K. L. M., Switzer, F., & Scott, D. (2007). Car following decisions under three visibility conditions and two speeds tested with a driving simulator. Accident Analysis & Prevention, 39(1), 106-116. DOI: https://doi.org/10.1016/j.aap.2006.06.009

Chakrabarty, N., & Gupta, K. (2013). Analysis of Driver Behaviour and Crash Characteristics during Adverse Weather Conditions. Procedia – Social and Behavioral Sciences, 104, 1048-1057. DOI: https://doi.org/10.1016/j.sbspro.2013.11.200

Deveaux, D., Higuchi, T., Uçar, S., Wang, C.-H., Härri, J., & Altintas, O. (2021). Extraction of Risk Knowledge from Time To Collision Variation in Roundabouts. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA, 3665-3672. DOI: https://doi.org/10.1109/ITSC48978.2021.9564570

DWD, (2023). Stundenwerte_WW_00662_19501001_20231231. Accessed Apr 3, 2024.

DWD, (2025). Deutscher Wetterdienst, Wettersymbole. Accessed May 15, 2025.

Eck, M. A., Konrad, C. E., Rayne, S., & Black, A. W. (2022). Understanding the Role of Rainfall Intensity on Relative Car Crash Risk in the Carolinas. Wea. Climate Soc., 14, 965-978. DOI: https://doi.org/10.1175/WCAS-D-22-0025.1

Elvik, R. (2007). State-of-the-art approaches to road accident black spot management and safety analysis of road networks. Transport Reviews, 27(5), 613-636.

European Commission (2024). Annual statistical report on road safety in the EU. Brussels: European Road Safety Observatory, Directorate General for Transport. Accessed Dec 3, 2025.

Federal Highway Administration (2024). About intersection safety. U.S. Department of Transportation. Accessed Mar 10, 2026.

European Union (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC. General Data Protection Regulation.

Khan, G., Xiao, Q., & Noyce, D. A. (2008). Spatial Analysis of Weather Crash Patterns. Journal of Transportation Engineering, 134(5), American Society of Civil Engineers. 191-202. DOI: https://doi.org/10.1061/(ASCE)0733-947X(2008)134:5(191)

Gomes, L. (2014). Urban jungle a tough challenge for Google autonomous cars. MIT Technology Review, Accessed Feb 18, 2026.

Gorzelanczyk, P. (2025). Impact of weather conditions and road type on traffic safety. European Transport Studies, 2, 100042. DOI: https://doi.org/10.1016/j.ets.2025.100042

Hammit, B. E., James, R., Ahmed, M., & Young, R. (2019). Toward the Development of Weather-Dependent Microsimulation Models. Transportation Research Record: Journal of the Transportation Research Board, 2673(7), 143-156. DOI: https://doi.org/10.1177/0361198119844743

Hancock, P. A., Kajaks, T., Caird, J. K., Chignell, M. H., Mizobuchi, S., Burns, P. C., Feng, J., Fernie, G. R., Lavallière, M., Noy, I. Y., Redelmeier, D. A., & Vrkljan, B. H. (2020). Challenges to Human Drivers in Increasingly Automated Vehicles. Hum Factors, 62(2), 310-328. DOI: https://doi.org/10.1177/0018720819900402

Hauer, E. (1997). Observational Before–After Studies in Road Safety: Estimating the Effect of Highway and Traffic Engineering Measures on Road Safety, Tarrytown, NY: Elsevier Science Incorporated.

Hayward, J. C. (1972). Near miss determination through use of a scale of danger. 51st Annual Meeting of the Highway Research Board, 384 Highway Research Board. 24-34.

Høye, A., Elvik, R., Sørensen, M. W. J., & Vaa, T. (2023). Traffiksikkerhetshåndboken, 2.6 Vinterdrift av veger [Traffic safety handbook, Chapter 2.6 Winter maintenance of roads]. Accessed Feb 16, 2026.

ITF (2022). The Safe System Approach in Action. Paris: OECD Publishing. Accessed Feb 18, 2026.

Junghans, M., Zhang, M., Saul, H., & Leich, A. (2024). Reliability of C-ADAS and the importance of the acceleration function for cycling safety. Traffic Safety Research, 7, e000074. DOI: https://doi.org/10.55329/zzsz4880

Junghans, M., Leschik, C., Schicktanz, C., & Wagner, P. (2025). Influence of weather on car-following at an urban intersection. 37th ICTCT Conference, Berlin, Germany, Oct 23–24.

Karim, M. R., Saifizul, A., Yamanaka, H., Sharizli, A., & Ramli, R. (2013). Minimum safe time gap (MSTG) as a new safety Indicator incorporating vehicle and driver factors. J. East. Asia Soc. Transp. Stud., 10, 2069-2079.

Kendall, G. (2017). Why UPS Drivers Don't Turn Left And You Probably Shouldn't Either. Accessed Mar 3, 2026. DOI: https://doi.org/10.64628/AB.c5npjvpda

Kilpeläinen, M., & Summala, H. (2007). Effects of weather and weather forecasts on driver behaviour. Transportation Research Part F: Traffic Psychology and Behaviour, 10(4), 288-299. DOI: https://doi.org/10.1016/j.trf.2006.11.002

Knake-Langhorst, S., & Gimm, K. (2016). AIM Research Intersection: Instrument for traffic detection and behavior assessment for a complex urban intersection. Journal of Large-Scale Research Facilities (JLSRF), 2. DOI: https://doi.org/10.17815/jlsrf-2-122

Lee, U., Jung, J., & Jung, S. (2018). Development of a self-driving car that can handle the adverse weather. International Journal of Automotive Technology, 19, 191-197. DOI: https://doi.org/10.1007/s12239-018-0018-z

Madjid, N. A., Abdulrahman, A., Mebrahtu, M., Babaa, Y., Nasser, A., Malik, S., Hassan, B., Werghi, N., Dias, J., & Khonji, M. (2025). Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions. arXiv:2503.03262v1. DOI: https://doi.org/10.1016/j.inffus.2025.103588

Malin, F., Norros, I., & Innamaa, S. (2019). Accident risk of road and weather conditions on different road types. Accident Analysis & Prevention, 122, 181-188. DOI: https://doi.org/10.1016/j.aap.2018.10.014

Mersch, B., Hollen, T., Zhao, K., Stachniss, C., & Roscher, R. (2021). Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks. arXiv:2109.07365v1. DOI: https://doi.org/10.1109/IROS51168.2021.9636875

National Highway Traffic Safety Administration (2010). Crash Factors in Intersection-Related Crashes: An On-Scene Perspective. Accessed Dec 3, 2025.

National Highway Traffic Safety Administration (2022). Fatality Analysis Reporting System (FARS) 2021 Final File and 2022 Annual Report File. Status: May 2024.

National Highway Traffic Safety Administration (2024). NHTSA Finalizes Key Safety Rule to Reduce Crashes and Save Lives. Accessed Dec 3, 2025.

Nikolaou, D., Ziakopoulos, A., & Yannis, G. (2023). A review of surrogate safety measures uses in historical crash investigations. Sustainability, 15(9), 7580. DOI: https://doi.org/10.3390/su15097580

Park, H., Oh, C., Moon, J., & Kim, S. (2018). Development of a lane change risk index using vehicle trajectory data. Accident Analysis & Prevention, 110, 1-8. DOI: https://doi.org/10.1016/j.aap.2017.10.015

Prisco, J. (2017). Why UPS trucks (almost) never turn left. CNN World, Accessed Mar 3, 2026.

Schicktanz, C., & Gimm, K. (2025). Detection and analysis of corner case scenarios at a signalized urban intersection. Accident Analysis & Prevention, 210, 107838. DOI: https://doi.org/10.1016/j.aap.2024.107838

SafetyCube DSS, (2020). Accessed Feb 18, 2026.

Shangguan, Q., Fu, T., & Liu, S. (2020). Investigating rear-end collision avoidance behavior under varied foggy weather conditions: A study using advanced driving simulator and survival analysis. Accident Analysis & Prevention, 139, 105499. DOI: https://doi.org/10.1016/j.aap.2020.105499

Shangguan, Q., Fu, T., Wang, J., Jiang, R., & Fang, S. (2021). Quantification of Rear-End Crash Risk and Analysis of Its Influencing Factors Based on a New Surrogate Safety Measure. Journal of Advanced Transportation, 1, 5551273. DOI: https://doi.org/10.1155/2021/5551273

Shawky, M. (2020). Factors affecting lane change crashes. IATSS Research, 44(2), 155-161. DOI: https://doi.org/10.1016/j.iatssr.2019.12.002

Stockem Novo, A., Hürten, C., Baumann, R., & Sieberg, P. (2023). Self-evaluation of automated vehicles based on physics, state-of-the-art motion prediction and user experience. Sci Rep., 13(1), 12692. DOI: https://doi.org/10.1038/s41598-023-39811-1

Sullivan, H. (2025). Motorwaymap.co.uk Traffic Analysis – The Impact of Weather Conditions on Motorway Traffic Flow. Accessed Nov 21, 2025.

Svensson, Å. (1998). A method for analysing the traffic process in a safety perspective. Doctoral Dissertation, Lund, Sweden: University of Lund.

SWOV Institute of Road Safety Research (2023). The impact of the weather. Accessed Nov 21, 2025.

Theofilatos, A., & Yannis, G. (2014). A review of the effect of traffic and weather characteristics on road safety. Accident Analysis & Prevention, 72, 244-256. DOI: https://doi.org/10.1016/j.aap.2014.06.017

Wagner, P., Nippold, R., & Grötsch, J. (2020). Extended Fundamental Diagrams. Transportation Research Record, 2674, 292-299. DOI: https://doi.org/10.1177/0361198120912235

Walker, C. L., Boyce, B., Albrecht, C. P., & Siems-Anderson, A. (2020). Will Weather Dampen Self-Driving Vehicles?. Bull. Amer. Meteor. Soc., 101, E1914-E1923. DOI: https://doi.org/10.1175/BAMS-D-19-0035.1

Wang, C., Xie, Y., Huang, H., & Liu, P. (2021). A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling. Accident Analysis & Prevention, 157, 106157. DOI: https://doi.org/10.1016/j.aap.2021.106157

Wang, Y., Xu, C., Liu, P., Li, Z., & Chen, K. (2024). Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes. Accident Analysis & Prevention, 201, 107573. DOI: https://doi.org/10.1016/j.aap.2024.107573

Wetterkontor (2019). Wetterrückblick Braunschweig März 2019 [Weather review Braunschweig March 2019]. Accessed May 20, 2026.

Wetterkontor, (2019). Wetterrückblick Braunschweig April 2019 [Weather review Braunschweig April 2019]. Accessed May 20, 2026.

Wetterkontor, (2019). Wetterrückblick Braunschweig May 2019 [Weather review Braunschweig May 2019]. Accessed May 20, 2026.

Xu, C., Wang, C., & Liu, P. (2018). Evaluating the Combined Effects of Weather and Real-Time Traffic Conditions on Freeway Crash Risks. Wea. Climate Soc., 10, 837-850. DOI: https://doi.org/10.1175/WCAS-D-17-0124.1

Wu, Y., Abdel-Aty, M., & Park, J. (2017). Developing a rear-end crash risk algorithm under fog conditions using real-time data. 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Naples, Italy, 568-573. DOI: https://doi.org/10.1109/MTITS.2017.8005736

Wu, Y., Abdel-Aty, M., & Lee, J. (2018). Crash risk analysis during fog conditions using real-time traffic data. Accident Analysis & Prevention, 114, 4-11. DOI: https://doi.org/10.1016/j.aap.2017.05.004

Yan, X., Li, X., Liu, Y., & Zhao, J. (2014). Effects of foggy conditions on drivers’ speed control behaviors at different risk levels. Safety Science, 68, 275-287. DOI: https://doi.org/10.1016/j.ssci.2014.04.013

Yu, R., Abdel-Aty, M. A., Ahmed, M. M., & Wang, X. (2014). Utilizing Microscopic Traffic and Weather Data to Analyze Real-Time Crash Patterns in the Context of Active Traffic Management. IEEE Transactions on Intelligent Transportation Systems, 15(1), 205-213. DOI: https://doi.org/10.1109/TITS.2013.2276089

Yu, M.-Y., Vasudevan, R., & Johnson-Roberson, M. (2019). Occlusion-Aware Risk Assessment for Autonomous Driving in Urban Environments. IEEE Robotics and Automation Letters, 4(2), 2235-2241. DOI: https://doi.org/10.1109/LRA.2019.2900453

Zeng, Q., Hao, W., Lee, J., & Chen, F. (2020). Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis. International Journal of Environmental Research and Public Health, 17(8), 2768. DOI: https://doi.org/10.3390/ijerph17082768

Zhang, W., Dai, J., Pei, Y., Li, P., Yan, Y., & Chen, X. (2016). Drivers’ Visual Search Patterns during Overtaking Maneuvers on Freeway. International Journal of Environmental Research and Public Health, 13(11), 1159. DOI: https://doi.org/10.3390/ijerph13111159

Zhang, Y., Carballo, A., Yang, H., & Takeda, K. (2023). Perception and sensing for autonomous vehicles under adverse weather conditions: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 146-177. DOI: https://doi.org/10.1016/j.isprsjprs.2022.12.021